Metric learning aims to learn a semantic embedding space, where similar samples are encouraged to be close together, while dissimilar ones are far apart. With the flourish of deep learning, deep metric learning (DML) has drawn much interest in recent years [1]}, [2]}, [3]}, [4]}.
Exploring and collecting informative negatives is of great importance in DML. To reach this, many efforts have been devoted to designing complicated pairwise sampling, mining or weighting strategies to capture more information during training, e.g., distance-weighted sampling in [5]}, log-sum-exp weighting schemes in [4]}, [7]}, and constructing pairs across preceding mini-batches from cached embeddings [8]}.
